Method for using facial expression to determine affective information in an imaging system

ABSTRACT

A method for determining affective information for at least one image in an imaging system includes displaying a digital image for viewing by a user; monitoring the facial expression of the user as the user views the digital image; and using the facial expression of the user to determine affective information for the digital image.

CROSS REFERENCE TO RELATED APPLICATIONS

Reference is made to commonly assigned U.S. patent application Ser. No.09/721,222, filed Nov. 22, 2000, entitled “Method for AddingPersonalized Metadata to a Collection of Digital Images” by Kenneth A.Parulski et al., now U.S. Pat. No. 6,629,104 issued Sep. 30, 2003; Ser.No. 10/036,113, filed Dec. 26, 2001, entitled “Method for Creating andUsing Affective Information in a Digital Imaging System” by TomaszMatraszek et al; Ser. No. 10/036,123, filed Dec. 26, 2001, entitled“Method for Using Affective Information Recorded With Digital Images forProducing an Album Page” by Tomasz Matraszek et al; Ser. No. 10/036,157,filed Dec. 26, 2001, entitled “An Image Format Including AffectiveInformation” by Tomasz Matraszek et al; Ser. No. 10/079,646, filed Feb.19, 2002 entitled “Method for Providing Affective Information in anImaging System” by Elena A. Fedorovskaya et al.; and Ser. No.10/079,283, filed Feb. 19, 2002 entitled “Method for Using Viewing Timeto Determine Affective Information in an Imaging System” by Elena A.Fedorovskaya et al., the disclosures of which are incorporated herein byreference.

FIELD OF THE INVENTION

The present invention relates to imaging systems and, more particularly,to systems which monitor a user's facial expression to determine theuser's reaction to digital images.

BACKGROUND OF THE INVENTION

Digital images, from digital cameras or scanned photographic film, canbe viewed, stored, retrieved, and printed using a home computer, or canbe uploaded to a website for viewing, as described in commonly assignedU.S. Pat. No. 5,666,215 to Fredlund et al. Using a web browser, a groupof these digital images can be viewed and selected for printing, or itcan be sent electronically to other family members and/or friends.

Currently, the usage of the Internet or personal computers and printingdevices for picture printing, sharing and storage is growing. Customerscreate large personal databases of images on the computers and webservers. It is becoming increasingly important to classify or catalogimages for subsequent use. Images can be organized into categoriesaccording to the people, places, subjects or events depicted, asdescribed in a paper entitled “FotoFile: A Consumer MultimediaOrganization and Retrieval System” by Kuchinsky et al. This paperdescribes such categories or attributes that are used to tag certainimages, including a “favorite” attribute that is loosely defined asreferring to the “best” images in a user's collection. Classifyingimages based on user's preference toward favorite images helps toquickly retrieve and share those valuable images. In this paper, the“favorite” attribute could only be used to mark and retrieve specificuser's images on their home PC, since there is nothing in the “favorite”attribute designating which user has indicated that this is a “favorite”image. Moreover, this attribute, as it is suggested by Kuchinsky et al.,does not allow any systematic differentiation with respect to the degreeof preference within the images already marked as favorite images. As aresult, after a certain time of acquiring images in the user's PCdatabase, the number of favorite images becomes too large to serve thepurpose of the favorite attribute, unless the user will change theattribute for every image in his or her database, which is a lengthy andtiresome process. In addition, the concept of the “best” image does notnecessarily refer to a user's emotional reaction.

Consequently, a need exists for an improved method for recording andinterpreting the user's emotional reaction to an image for subsequentassociation of this affective information with a corresponding image anda user identifier.

The present invention broadly defines affective information associatedwith the image to include various types of psychological reactions, suchas affective, cognitive, physiological, or behavioral responses that arenot found in any previous systems. It refers both to recorded rawsignals and their interpretations.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide affectiveinformation for images in an imaging system.

This object is achieved by a method for determining affectiveinformation for at least one image in an imaging system, comprising thesteps of:

a) displaying a digital image for viewing by a user;

b) monitoring the facial expression of the user as the user views thedigital image; and

c) using the facial expression of the user to determine affectiveinformation for the digital image.

ADVANTAGES

It is an advantage of the present invention to provide personalizedaffective information associated with digital images. This informationprovides for unique personal classification of such digital images forfuture possible usage, e.g. retrieval, communication and sharing,advertising and marketing.

It is an additional advantage of the present invention that affectiveinformation can be determined from the user's facial expression.

It is a further advantage of the present invention to achieve a largenumber of different affective categories such as happy, sad, angry, etc.for classifying digital images.

It is also an advantage of the present invention to continuously updateexisting affective information for classifying images by considering newaffective information about a user's reaction to existing images, aswell as new images added to an image database.

It is a further advantage of the present invention that affectiveinformation can be associated with the image at different times. Thishistory of user's reaction to a given image enables analysis of changesin person's reaction that can be used for therapeutic, diagnostic, orretrospective purposes.

It is an additional advantage of the present invention that affectiveinformation is associated with a user identifier.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a block diagram of home computer system for providingaffective information;

FIG. 2 is a flow diagram showing an embodiment of a system for providingaffective information based on analysis of facial expressions;

FIG. 3 is a flow diagram showing an embodiment of a system for providingaffective information based on analysis of viewing time;

FIG. 4 is a flow diagram showing an embodiment of a system for providingaffective information based on analysis of skin conductance;

FIG. 5 is a flow diagram showing an embodiment of a system for providingaffective information based on combined analysis of facial expressions,viewing time, and skin conductance;

FIG. 6A is an example of a simple personal affective tag for a singleuser; and

FIG. 6B is an example of affective metadata for multiple users withmultiple personal affective tags.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a structure and method for capturing auser's reaction during the process of image viewing, such as on adisplay of a personal computer; and interpreting this reaction in termsof a degree of preference, importance, or a certain emotional categoryfor subsequent association of this information and its interpretationwith a specified image. The information about user's reaction to theimage is based on automatically recording one or more of the followingsignals using physical or bio-metrical devices: the duration of timethat the user views a certain image; the facial expression of the userwhile viewing the image; and the skin conductance of the user whileviewing the image. Interpretation of the recorded information producesseveral gradations of user's preference, e.g. the degree to which theuser likes the image and considers it to be his or her favorite image.It also provides a relative degree of importance or interest of theimage to the user. Additionally, interpretation of such informationclassifies the recorded information in terms of the specific emotion,for example, happy or sad.

By images is meant still or moving images, or multimedia clips orfragments that include visual information. People create images for avariety of purposes and applications. Capturing memorable events is oneexample of an activity that ordinary people, professional photographers,or journalists alike have in common. These events are meaningful oremotionally important to an individual or a group of individuals. Imagesof such events attract special attention, elicit memories,feelings/emotions, or specific behavior. One can say that these picturesof special events and moments evoke certain mental or behavioralreactions or, in general terms, psychological reactions.

Often these psychological reactions are accompanied by physiologicalchanges. Relevant information that represents or describes a particularuser's reactions toward images in terms of user's affective, cognitive,physiological or behavioral responses are referred to in the presentinvention as affective information. The affective information can beattributable to memories and associations related to depicted events, aswell as to a particular rendering of an image (image quality, beautifulpicture, peculiar composition, specific people, etc.).

Affective tagging is defined as the process of determining affectiveinformation, and storing the affective information in association withthe images. When the affective information is stored in association witha user identifier, it is referred to in the present invention as“personal affective information”. When the personal affectiveinformation is stored in association with the corresponding image, it isreferred to as “personal affective tag”. The affective information anduser identifier are types of image “metadata”, which is a term used forany information relating to an image. Examples of other types of imagemetadata include image capture time, capture device, capture location,date of capture, image capture parameters, image editing history etc.

Affective information can be manually entered by a user, for example,using a mouse, keyboard, or voice recognition, or it can be collectedautomatically. The following are examples of methods for automaticallycollecting affective information:

-   The viewing time of the image, since a longer viewing time normally    indicates that this is an important image;-   Other behavioral/usage information related to the usage of this    image, such as how many times the image was printed or sent to    others via e-mail;-   The facial expression of the user, which can be captured    automatically by a video camera as the user views the image;-   Body gestures recorded by a video camera as the user views the    image;-   The user's eye movements, such as the gaze path of the user while    viewing the image;-   The user's voice, recorded while viewing the image;-   The pressure of the user's hand on the input device (e.g. joystick    or mouse) recorded while viewing the image; and-   The user's biometric or physiological responses recorded as the user    views the image. These can be any combination of signals, including    galvanic skin response (GSR), EMG, Temperature, GSR, Heart Rate,    brain waves EEG, brain-imaging signals, etc.

Affective information is determined automatically based on viewing time,the facial expression, and galvanic skin response.

Referring to FIG. 1, there is illustrated a desktop computer system madein accordance with the present invention for providing personalaffective information, associating it with image(s) as image metadata,and storing the images and the associated personal affectiveinformation.

The system depicted in FIG. 1 includes a home computer 10 (withassociated peripherals) located at the user location (e.g. their home).It is understood that a system such as a TV set, game console, dedicatedInternet appliance, set top box, Wireless PDA devices, telephone withthe embedded display, retail kiosks, electronic picture frame devices,etc. may be substituted for the home computer system 10.

The home computer 10 is connected through a modem 22 or othercommunications interface to a communication service provider, such as anInternet service provider 30. The home computer 10 includes a CPUmotherboard 12, a display monitor 14, and a hard drive storage device 20that can store high resolution image files such as digital still ordigital motion images and associated metadata.

The home computer includes various image input peripherals, including ascanner 28, a CD reader 26, and a digital camera 24. The scanner 28 isused to scan prints or film and produce digital images. The CD reader 26is used to input digital images from a CD-R disk, such as a KodakPictureCD (not shown). The user can also download images from a ServiceProvider using the modem 22. The home computer 10 can also connect to alocal printer (not shown), such as an inkjet printer, to allow images tobe printed at the user's home.

The digital camera 24 can be a digital still camera such as the Kodak DC4800 digital camera manufactured by the Eastman Kodak Company,Rochester, N.Y. Alternatively, the digital camera 24 can be a digitalmotion camera such as the Kodak MC3 portable multimedia device, whichrecords motion and sound video files, in addition to still images. Thedigital camera 24 connects to the home computer 10 via a cable employinga common interface, such as the well known Universal Serial Bus (USB)interface or the IEEE 1394 interface. Alternatively, digital camera 24can connect to the home computer 10 using a wireless interface, such asthe well known Bluetooth interface or the IEEE Standard 802.15interface. Alternatively, the images can be transferred from the digitalcamera 24 to the home computer 10 using a removable memory card, such asa CompactFlash card and a card reader (not shown).

In order to provide affective information, the home computer 10 isconnected to a keyboard 16 and a pointing device 18, such as a mouse ortrackball. In a preferred embodiment, the pointing device 18 alsoincludes sensors that can detect the user's physiological signals,including the GSR (Galvanic Skin Response). The home computer 10 is alsoconnected to video camera 4. The video camera is also a sensor whichcaptures video images of the face of the user 2, in order to record thefacial expression of the user, and stores this video information on thehard drive storage 20 prior to processing by the CPU motherboard 12 Thevideo camera 4 can be, for example, a DV325 tethered camera sold byEastman Kodak Company. The camera connects to the home computer 10 via acable employing a common interface, such as the Universal Serial Bus(USB) interface.

The home computer 10 is provided with appropriate software for creatingand using personalized affective information in accordance with thepresent invention. This software is typically stored on hard drive 20,and can be provided on a CD-ROM disc (not shown). Alternatively, thissoftware can be downloaded from the Internet via modem 22.

In a preferred embodiment, the home computer 10 determines affectiveinformation based on one of the following: a) a degree of preferenceextracted from facial expression; b) a degree of interest extracted fromviewing time; and c) a degree of excitement extracted from galvanic skinresponse, or a combination of this information.

Referring to FIG. 2, there is shown a flow diagram illustratingembodiment of the present invention for providing affective informationbased on the degree of preference of a particular user for each of a setof digital images. In this embodiment, affective information indicatinga degree of preference for each image from a plurality of digital imagesis determined based on facial expression of the particular user.

In block 110, the user opens a new set of images by inserting the CD-ROMinto the CD reader 26 of the home computer 10. The CD-ROM provides adatabase of digital images. Alternatively, the set of digital images canbe provided using many other types of digital storage media, includingmagnetic disks and tape, optical discs, and solid-state memory. In apreferred embodiment, the CD-ROM also includes the software applicationthat implements the method of FIG. 2. In this case, the software isautomatically installed as part of block 112, if necessary, so that itcan be used by CPU motherboard 12.

In block 112, the application which implements the method of the presentinvention is launched automatically, when a user views the images forthe first time. Alternatively, the user can start the applicationmanually and load a new set of images from the digital camera 24, thescanner 28, or from other image sources including the Internet.

In block 114, the user enters their personal ID and password.Alternatively, this step can be provided automatically by the operatingsystem of the home computer 10 when the user “logs on” to the computer.In an alternative embodiment, the video camera 4 is used in conjunctionwith face recognition software to automatically determine the user, andprovide an appropriate user identifier, such as their name or personalidentification code.

In block 116, the home computer 10 provides a selection of signals thatcan be recorded in order to determine the user's emotional reaction asthey view images.

In block 118, the user selects the desirable signal, i.e., facialexpression in this case. In block 120, the home computer 10 retrievesthe first image from the CD-ROM (or other image source) and in block122, the home computer 10 displays the image on the monitor 14.

In block 124, the home computer 10 automatically analyses the facialexpression of the user during the process of viewing images byprocessing images captured by the video camera 4. Frames of the videoinformation are processed using a facial expression recognitionalgorithm. Facial expressions can be analyzed using a publicly disclosedalgorithm for facial expression recognition such as an algorithmdeveloped by Essa and Pentland (I. A. Essa and A. Pentland, [1995]“Facial Expression Recognition using a Dynamic Model and Motion Energy”,In Proceedings of the ICCV 95, Cambridge, Mass.). Their algorithm isbased on the knowledge of the probability distribution of the facialmuscle activation associated with each expression and a detailedphysical model of the skin and muscles. The physics-based model is usedto recognize facial expressions through comparison of estimated muscleactivations from the video signal and typical muscle activationsobtained from a video database of emotional expressions.

Facial expressions can also be analyzed by means of other publiclyavailable algorithms (e.g., J. J. Lien, T. Kanade, J. F. Cohn and C. C.Li, (2000) “Detection, Tracking, and Classification of Action Units inFacial Expression,” Robotics and Autonomous Systems, 31, pp. 131–146,2000; Bartlett, M. S., Hager, J. C., Ekman, P., and Sejnowski, T. J.,[1999] “Measuring facial expressions by computer image analysis”,Psychophysiology, 36, pp. 253–263). Their algorithms are based onrecognizing specific facial actions —the basic muscle movements—whichwere described by Ekman and Friesen (P. Ekman and W. Friesen, [1978]“Facial Action Coding System”, Consulting Psychologists Press, Inc.,Palo Alto, Calif.) in the Facial Action Coding System (FACS). The basicfacial actions can be combined to represent any facial expressions. Forexample, a spontaneous smile can be represented by two basic facialactions: 1) the corners of the mouth are lifted up by a muscle calledzygomaticus major; and 2) the eyes are crinkled by a muscle calledorbicularis oculi. Therefore, when uplifted mouth and crinkled eyes aredetected in the video signal, it means that a person is smiling. As aresult of the facial expression analysis, a user's face can berecognized as smiling when a smile on user's face is detected, or notsmiling when the smile is not detected.

In block 126, the home computer 10 determines the smile size. If thesmile is not detected, the smile size equals 0. If a smile has beendetected for a given image _(I), a smile size _(S) _(I) for the image_(I) is determined as the maximum distance between mouth corners withinfirst three seconds after the onset of the specified image divided bythe distance between the person's eyes. The distance between theperson's eyes is determined using the facial recognition algorithmsmentioned above. The necessity of taking the ratio between the size ofthe mouth and some measure related to the head of the person (e.g. thedistance between the eyes) stems from the fact that the size of themouth extracted from the video frame depends on the distance of the userto the video camera, position of the head, etc. The distance between theperson's eyes is used to account for this dependency, however, othermeasures such as the height or width of the face, the area of the faceand others measures can also be used.

In block 128, the home computer 10 determines the degree of preference.The degree of preference for the image _(I) is defined as the smile size_(S) _(I) for the image _(I). If the smile was not detected, then thesmile size and consequently the degree of preference is equal to 0.

In block 130, the home computer 10 creates a personal affective tag forthe image _(I) and stores the degree of preference in the personalaffective tag as part of the image metadata. Alternatively, the degreeof preference can be stored in a separate file in association with theuser identifier and the image identifier.

In addition, the information about the date the user views a certainimage can be also recorded as a separate entry into personal affectivetag. Every time the user views the specified image a new personalaffective tag is created which contains the user identifier, the degreeof preference and the date when the image was viewed. As a result,images that were viewed more frequently would contain a bigger number ofpersonal affective tags.

In block 132, the user clicks the pointing device 18 to indicate thatthey want to move to the next image. Alternatively, the user can providesome other form of input, such as hitting a key on the keyboard 16,providing an audio command, which is input by a microphone (not shown),providing a gesture captured by video camera 4, or using other inputdevices.

In block 134, the home computer 10 determines if this is the last imageof the image set.

In block 136, if this is not the last image, the home computer 10retrieves the next image of the image set and repeats blocks 122 through134.

In block 138, if this is the last image, the home computer 10 stops theprocess of affective tagging.

Through the process of affective tagging the degree of preference can bedetermined and updated for all images in the database that were viewedby the user.

The degree of preference can be used in a digital imaging system to rankimages in a systematic and continuous manner as favorite images for aspecified user. This ranking can be done either based on the maximumdegree of preference for each image (chosen among all personal affectivetags created for this user and a specified image) or based on thecumulative degree of preference, which is defined as the sum of thedegrees of preference for this user extracted from all relevant personalaffective tags for the specified image.

The ranking can also be done for a specific time period. In this case,only the personal affective tags created during a desired period of timeare considered.

In another embodiment, a binary degree of preference for images in animage database can be determined. When the smile is detected in block124, the corresponding image is then classified as preferred with thebinary degree of preference equals 1. Alternatively, when the smile isnot detected, the image is classified as not preferred with the degreeof preference equals 0.

The determined affective information in terms of the binary degree ofpreference is then stored as personal affective tag, which includes theuser identifier as part of the image metadata. It can also be stored ina separate file on the computer together with the image identifier andthe user identifier. In addition, affective information in terms of theactual frame(s) of the user's facial expression can also be stored in aseparate file in association with the image identifier and the useridentifier.

Yet in another embodiment, emotional category for images in an imagedatabase can be determined. The facial expression may be classified intoa broader range of emotional categories, such as ‘happy’, ‘sad’,‘disgust’, ‘surprised’, etc. As a result of facial recognition, imagesthat evoke ‘happy’ facial expressions are assigned the ‘happy’ emotionalcategory, images that evoke ‘sad’ facial expressions are assigned the‘sad’ emotional category, etc. Images can be further classified using arange of values for these categories, such as strongly happy, somewhathappy, neutral and somewhat sad, and strongly sad, etc.

The determined affective information in terms of the emotional categoryis then stored as personal affective tag, which includes the useridentifier as part of the image metadata. It can also be stored in aseparate file on the computer together with the image identifier and theuser identifier.

Referring to FIG. 3, there is shown a flow diagram illustrating anotherembodiment of the present invention for providing affective informationbased on the degree of interest of the particular user to a plurality ofdigital images. With this embodiment, a degree of interest is determinedbased on the viewing time, which is the time that the user views eachdigital image, before moving on to the next digital image.

The data described in a paper entitled “Looking at pictures: Affective,facial, visceral, and behavioral reactions”, Psychophysiology, 30, pp.261–273, 1993, by P. J. Lang, M. K. Greenwald, M. M. Bradley, and A. O.Hamm, indicates that on average, viewing time linearly correlates withthe degree of the interest or attention an image elicit in an observer.Thus, such a relationship allows interpreting the viewing time as theuser's degree of interest toward a specified image. Quoted publicationby Lang et al. compares a viewing time with the degree of the interestfor third party pictures only. In the present invention, a viewing timeinformation is assessed for every individual for the first party as wellas third party images and stored as a personal affective tag as part ofthe image metadata or in a separate file in association with the useridentifier and the image identifier.

Recording of this signal implies that a user controls the time duringwhich he or she observes an image. In the preferred embodiment, the userviews images on the monitor screen of the PC and proceeds to the nextimage by pressing the mouse button or hitting a key. The followingmethod to determine a degree of interest for every user and for everyimage is suggested and shown in FIG. 3.

In blocks 210 through 222, the method is the same as in blocks 110through 122 in FIG. 2. In block 224, the home computer 10 determines thetime interval _(T) _(IJ) between two consecutive images _(I) and _(J).

In block 226, the home computer 10 determines the degree of interest.The degree of interest for the image _(I) is defined as the timeinterval _(T) _(IJ) when the image _(I) was viewed.

To ensure that the viewing time is not improperly judged as a result ofuser distraction, the video camera 4 can be used to ensure that the user2 is directing their gaze towards the monitor 14, and is not distractedby other tasks, or has even left the room.

In block 228, the home computer 10 creates a personal affective tag forthe image _(I) and stores the degree of interest in the personalaffective tag as part of the image metadata. Alternatively, the degreeof interest can be stored in a separate file in association with theuser identifier and the image identifier.

In addition, the information about the date the user views a certainimage can be also recorded as a separate entry into personal affectivetag. Every time the user views the specified image a new personalaffective tag is created which contains the user identifier, the degreeof interest and the date when the image was viewed. As a result, imagesthat were viewed more frequently would contain a bigger number ofpersonal affective tags.

In blocks 230 through 236, the degree of interest is determined for allimages in the database that were viewed by the user.

The degree of interest can be used in a digital imaging system to rankimages in a systematic and continuous manner as favorite or importantimages for a specified user. This ranking can be done either based onthe maximum degree of interest for each image (chosen among all personalaffective tags created for this user and a specified image) or based onthe cumulative degree of interest, which is defined as the sum of thedegrees of interest for this user extracted from all relevant personalaffective tags for the specified image.

The ranking can also be done for a specific time period. In this case,only the personal affective tags created during a desired period of timeare considered.

Referring to FIG. 4, there is shown a flow diagram illustratingembodiments of the present invention for providing affective informationbased on the degree of excitement. With the present invention, a degreeof excitement is determined based on the skin conductance.

Skin conductance is a measure of galvanic skin response. Skinconductance reflects a magnitude of the electrical conductance of theskin that is measured as a response to a certain event —viewing theimage. As described in the paper “Looking at pictures: Affective,facial, visceral, and behavioral reactions”, _(Psychophysiology), 30,pp. 261–273, 1993, by P. J. Lang, M. K. Greenwald, M. M. Bradley, and A.O. Hamm, skin conductance changes depending on the arousal the imageelicits in the viewer: the higher the conductance, the lower the arousalor excitement, and vice versa: the lower the conductance, the higher thearousal. The measure of the amplitude of the skin conductance is alsoused to conclude about interest or attention.

The following method to determine a degree of excitement for every userand for every image is suggested and shown in FIG. 4.

In blocks 310 through 322, the method is the same as in blocks 210through 222 in FIG. 3. In block 324, the home computer 10 determines thechange in the skin conductance or _(C) _(I) during viewing an image_(I). Skin conductance signal is detected through sensors in pointingdevice 18. The pointing device 18 can be a computer mouse such as IMBcomputer mouse with special bio-metric sensor that is able to detect andrecord skin conductance. Other devices can also be used such as variouswearable devices for affective computing (computing emotional responsesfrom physiological signals) developed at the MIT Media Lab(http://www.media.mit.edu/affect/AC_research/wearables.html). An exampleof such device is the Galvactivator, a glove-like wearable device thatsenses the wearer's skin conductivity and maps its values to a brightLED display, created by Rosalind Picard and her colleagues(http://www.media.mit.edu/galvactivator/).

In block 326, the home computer 10 determines the degree of excitement.The degree of excitement for the image _(I) is defined as the skinconductance _(C) _(I) when the image _(I) was viewed.

To ensure that the change in skin conductance is elicited by the imageand not by any other extraneous events, the video camera 4 can be usedto check that the user 2 is directing their gaze towards the monitor 14,and is not distracted by other tasks, or has even left the room.

In block 328, the home computer 10 creates a personal affective tag forthe image _(I) and stores the degree of excitement in the personalaffective tag as part of the image metadata. Alternatively, the degreeof excitement can be stored in a separate file in association with theuser identifier and the image identifier.

In addition, the information about the date the user views a certainimage can also be recorded as a separate entry into personal affectivetag. Every time the user views the specified image a new personalaffective tag is created which contains the user identifier, the degreeof excitement and the date when the image was viewed. As a result imagesthat were viewed more frequently would contained a bigger number ofpersonal affective tags.

In blocks 330 through 336, the degree of excitement is determined forall images in the database that were viewed by the user.

The degree of excitement can be used in a digital imaging system to rankimages in a systematic and continuous manner as favorite, important orexciting images for a specified user. This ranking can be done eitherbased on the maximum degree of excitement for each image (chosen amongall personal affective tags created for this user and a specified image)or based on the cumulative degree of excitement, which is defined as thesum of the degrees of excitement for this user extracted from allrelevant personal affective tags for the specified image.

The ranking can also be done for a specific time period. In this case,only the personal affective tags created during a desired period of timeare considered.

In another embodiment the actual signal of galvanic skin response isstored as affective information either in a separate file on thecomputer 10 together with the image identifier and the user identifier,or in the personal affective tag as part of the image metadata.

Referring to FIG. 5, there is shown a flow diagram illustratingembodiment of the present invention for providing affective informationbased on the combination of the three affective signals described in theearlier paragraphs, namely, the degree of preference, the degree ofinterest and the degree of excitement, which are further combined toobtain an integral measure of positive importance.

In blocks 410 through 422, the method is the same as in blocks 210through 222 in FIG. 2. In block 424, the home computer 10 determines thedegree of preference based on facial expression (DP) the same way as inblock 128 of FIG. 3. In block 426, the home computer 10 determines thedegree of interest based on viewing time (DI) the same way as in block226 of FIG. 4. In block 428, the home computer 10 determines the degreeof excitement based on skin conductance (DE) the same way as in block326 of FIG. 4.

In block 430, the home computer 10 determines the degree of positiveimportance (or “favoriteness”) based on a sum of these three measures:Positive Importance=DP+DI+DE

In another embodiment, the degree of positive importance is determinedbased on a weighted sum of these three measures, where the weights aredetermined based on the standard deviation within each of the normalized(divided by the maximum value) signals over the image set. In this case,the higher the standard deviation within the signal, the higher theweight of the contribution for the signal into the measure of positiveimportance. Consequently, the lower the standard deviation of a givensignal, the lower the weight of the contribution for the correspondingsignal into the measure of positive importance. The reason for thisdependency stems from the assumption that a standard deviation of aparticular measure reflects a degree of differentiation between theimages along a given measure. This implies that the signal with thehighest standard deviation has more differentiation power, and thereforeis more important to consider while determining an integral measure ofpositive importance.

In block 432, the home computer 10 creates a personal affective tag forthe image _(I) and stores the degree of positive importance in thepersonal affective tag as part of the image metadata. Alternatively, thedegree of positive importance can be stored in a separate file inassociation with the user identifier and the image identifier.

In addition, the information about the date the user views a certainimage can also be recorded as a separate entry into personal affectivetag. Every time the user views the specified image a new personalaffective tag is created which contains the user identifier, the degreeof positive importance and the date when the image was viewed. As aresult images that were viewed more frequently would contained a biggernumber of personal affective tags.

In blocks 434 through 440, the degree of positive importance isdetermined for all images in the database that were viewed by the user.

The degree of positive importance can be used in a digital imagingsystem to rank images in a systematic and continuous manner as favoriteimages for a specified user. This ranking can be done either based onthe maximum degree of positive importance for each image (chosen amongall personal affective tags created for this user and a specified image)or based on the cumulative degree of positive importance, which isdefined as the sum of the degrees of positive importance for this userextracted from all relevant personal affective tags for the specifiedimage.

The ranking can also be done for a specific time period. In this caseonly the personal affective tags created during a desired period of timeare considered.

In another embodiment, different combinations of these three or otheraffective signals (such as derived from EEG, EMG, hand temperature,brain scan, eye movements and others) can be used to create the personalaffective tag to classify images in accordance with a broader range ofemotional categories, such as ‘happy’, ‘sad’, ‘disgust’, ‘surprised’,etc. Images can be further classified using a range of values for thesecategories, such as strongly happy, somewhat happy, neutral and somewhatsad, and strongly sad, etc.

The determined affective information in terms of the emotional categoryis then stored as personal affective tag, which includes the useridentifier as part of the image metadata. It can also be stored in aseparate file on the computer together with the image identifier and theuser identifier.

An illustrative example of a personal affective tag is shown in FIG. 6A.FIG. 6A depicts a file data structure for the simplest personalaffective tag for a single user, which includes a personalidentification field, and an affective field.

A personal identification code is stored in the personal identificationfield. This field identifies the user, whose affective information isstored in the personal affective tag.

Affective information is stored in the affective field. The affectiveinformation can be the result of automatic detection or a manual entryby the user.

The affective field identifies relevant data that represents ordescribes user's reactions toward images in terms of user's affective,cognitive, physiological, or behavioral responses. These data can beboth raw recorded signals (e.g., skin conductance response) and/orinterpreted information (e.g., the degree of positive importance). Theaffective field can also include basic emotion (e.g. happy) with acorresponding ranking that quantifies the intensity of the detectedemotion.

The minimum required information contained in a personal affective tagconsists of the personal identification code stored in the correspondingfield, and affective information stored in the affective field. Otheroptional fields such as date and place of viewing, or other informationcan be included in a personal affective tag.

An illustrative example of personal affective tags with optional fieldsis shown in FIG. 6A. Referring to FIG. 6B, there is shown an exampledata structure of affective metadata for a single image, which providespersonalized affective information for multiple users. Personalaffective tag #1 indicates that on Sep. 1, 2000, a computer stored thefollowing affective information for the first user (user 1) viewing thisimage: Facial Expression=Smile; Smile size=1.5; The Degree ofPreference=1.5; Emotional category=Strongly happy; Viewing time=15 sec;The Degree of Interest=15; Skin Conductance Response=5 _(μ)mho; TheDegree of Excitement=5; The Degree of Positive Importance=21.5.

Personal affective tag #2 indicates that later during the day of Sep. 1,2000 the computer stored the following affective information for thesecond user (user 2) viewing the specified image: Facial Expression=NoSmile; Smile size=0; The Degree of Preference=0; Emotionalcategory=Neutral; Viewing time=2 sec; The Degree of Interest=2; SkinConductance=1 _(μ)mho; The Degree of Excitement=1; The Degree ofPositive Importance=3.

According to the affective information, the specified image had a higherdegree of preference, interest, excitement, and importance for the firstuser than for the second user.

Personal affective tag #3 indicates that on Oct. 1, 2000 the computerstored the following affective information for the first user (user 1)viewing the specified image: Facial Expression=Smile; Smile size=1.1;The Degree of Preference=1.1; Emotional category=Somewhat happy; Viewingtime=10 sec; The Degree of Interest=10; Skin Conductance=3 _(μ)mho; TheDegree of Excitement=3; The Degree of Positive Importance=14.1.

According to this affective information, one month later the specifiedimage slightly decreased its degree of preference, interest, excitement,and importance for the first user.

The method for providing affective information described in detailpreviously in the present invention for the case of picture viewing canalso be utilized during the process of picture taking. In this case, theimaging capture device would need to be supplied with for example, abio-sensor and ability to capture the face of a picture taker.

The present invention can be used as part of a system for retrievingimages using affective information, and for producing album pages andother hardcopy photo products using affective information, as describedin commonly assigned U.S. patent application Ser. No. 10/079,646, filedconcurrently herewith entitled “Method for Providing AffectiveInformation in an Imaging System” by Elena A. Fedorovskaya et al.; andSer. No. 10/079,283, filed concurrently herewith entitled “Method forUsing Viewing Time to Determine Affective Information in an ImagingSystem” by Elena A. Fedorovskaya et al.; the disclosures of which areincorporated herein by reference.

A computer program product can include one or more storage medium, forexample; magnetic storage media such as magnetic disk (such as a floppydisk) or magnetic tape; optical storage media such as optical disk,optical tape, or machine readable bar code; solid-state electronicstorage devices such as random access memory (RAM), or read-only memory(ROM); or any other physical device or media employed to store acomputer program having instructions for practicing a method accordingto the present invention.

The invention has been described in detail with particular reference tocertain preferred embodiments thereof, but it will be understood thatvariations and modifications can be effected within the spirit and scopeof the invention.

PARTS LIST

-   2 user-   4 video camera-   10 home computer systems-   12 CPU motherboard-   14 monitor-   16 keyboard-   18 pointing device with physiology sensors-   20 hard drive-   22 modem-   24 digital still camera-   26 CD reader-   28 scanner-   30 Internet service provider-   32 modem-   110 block-   112 block-   114 block-   116 block-   118 block-   120 block-   122 block-   124 block-   126 block-   128 block-   130 block-   132 block-   134 block-   136 block-   138 block-   210 block-   212 block-   214 block-   216 block-   218 block-   220 block-   222 block-   224 block-   226 block-   228 block-   230 block-   232 block-   234 block-   236 block-   310 block-   312 block-   314 block-   316 block-   318 block-   320 block-   322 block-   324 block-   326 block-   328 block-   330 block-   332 block-   334 block-   336 block-   410 block-   412 block-   414 block-   416 block-   418 block-   420 block-   422 block-   424 block-   426 block-   428 block-   430 block-   432 block-   434 block-   436 block-   438 block-   440 block

1. A method for determining affective information for at least one imagein an imaging system, comprising the steps of: a) displaying a digitalimage for viewing by a user; b) using a video camera to monitor thefacial expression of the user as the user views the digital image todetermine the smile size of the user; and c) using the smile size of theuser to determine affective information for the digital image.
 2. Themethod of claim 1 further including the step of: d) associating theaffective information with the digital image.
 3. The method of claim 1wherein a plurality of digital images are displayed for viewing by theuser.
 4. The method of claim 3 wherein the smile size of the user isdetermined for each of the plurality of digital images.
 5. The method ofclaim 4 wherein a degree of preference is determined for each of theplurality of digital images by relating the smile size corresponding toeach digital image to an average smile size.
 6. The method of claim 5wherein the degree of preference is stored along with the correspondingdigital image in separate digital image files.
 7. A method for providingaffective information for images in an imaging system, comprising thesteps of: a) sequentially displaying a plurality of digital images forviewing by a user; b) using a video camera to monitor the facialexpression of the user as the user views each of the plurality ofdigital images and analyzing the images to determine the smile size ofthe user; and c) using the smile size of the user to determine affectiveinformation.
 8. A system for providing affective information for imagesin an imaging system, comprising: a) a digital memory which stores a setof digital images; b) a display which sequentially displays the set ofdigital images for viewing by a user; c) a video camera for capturingthe user's facial expression; and d) a processor for processing thesignal from the video camera to determine the user's smile size in orderto provide affective information for the set of digital images.
 9. Thesystem of claim 8 wherein the process determines the normalized smilesize for each digital image in the set.
 10. The system of claim 8wherein the smile size determine using the maximum distance betweenmouth corners.
 11. The system of claim 8 wherein the system furtherincludes a sensor for measuring the user's physiology.
 12. The system ofclaim 11 wherein the sensor measures the user's galvanic skin response.13. The system of claim 8 wherein the affective information is stored inthe digital memory.
 14. The system of claim 8 wherein the affectiveinformation is stored with each digital image in a digital image file.15. The system of claim 14 wherein the digital image file includesaffective information and user identifiers for a plurality of users. 16.A method for determining affective information for at least one image inan imaging system, comprising the steps of: a) displaying a plurality ofdigital images for viewing by a user; b) monitoring the facialexpression of the user as the user views each of the plurality ofdigital images to determine the smile size of the user; and c) using thesmile size of the user to determine affective information for each ofthe plurality of digital images.
 17. The method of claim 16 wherein adegree of preference is determined for each of the plurality of digitalimages by relating the smile size corresponding to each digital image toan average smile size.
 18. The method of claim 16 wherein the degree ofpreference is stored along with the corresponding digital image inseparate digital image files.